Nearest-neighbor searching under uncertainty

Pankaj K. Agarwal, Alon Efrat, Swaminathan Sankararaman, Wuzhou Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

44 Scopus citations

Abstract

Nearest-neighbor queries, which ask for returning the nearest neighbor of a query point in a set of points, are important and widely studied in many fields because of a wide range of applications. In many of these applications, such as sensor databases, location based services, face recognition, and mobile data, the location of data is imprecise. We therefore study nearest neighbor queries in a probabilistic framework in which the location of each input point and/or query point is specified as a probability density function and the goal is to return the point that minimizes the expected distance, which we refer to as the expected nearest neighbor (ENN). We present methods for computing an exact ENN or an ε-approximate ENN, for a given error parameter 0 < ε < 1, under dierent distance functions. These methods build an index of near-linear size and answer ENN queries in polylogarithmic or sublinear time, depending on the underlying function. As far as we know, these are the first nontrivial methods for answering exact or ε-approximate ENN queries with provable performance guarantees.

Original languageEnglish (US)
Title of host publicationPODS '12 - Proceedings of the 31st Symposium on Principles of Database Systems
Pages225-236
Number of pages12
DOIs
StatePublished - 2012
Event31st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS '12 - Scottsdale, AZ, United States
Duration: May 21 2012May 23 2012

Publication series

NameProceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems

Other

Other31st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, PODS '12
Country/TerritoryUnited States
CityScottsdale, AZ
Period5/21/125/23/12

Keywords

  • approximate nearest neighbor
  • expected nearest neighbor (enn)
  • indexing uncertain data
  • nearest-neighbor queries

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture

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